Characterizing population-level changes in human behavior during the COVID-19 pandemic in the United States

• 成果类型: Article
• 署名作者: Urmi, Tamanna; Pant, Binod; Dewey, George; Quintana-Mathe, Alexi; Lang, Iris; Druckman, James; Ognyanova, Katherine; Baum, Matthew; Perlis, Roy; Riedl, Christoph; Lazer, David; Santillana, Mauricio
• 署名单位: Northeastern University; Northeastern University; Harvard University; University of Rochester; Harvard University; Harvard University; Harvard University; Harvard Medical School; Harvard University Medical Affiliates; Massachusetts General Hospital; Northeastern University; Northeastern University; Harvard University; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11210
• DOI: 10.1073/pnas.2500655122
• 发表日期: 2025-09-16
• 关键词: infectious-diseases; human mobility; IMPACT

摘要

The transmission of communicable diseases in human populations is known to be modulated by behavioral patterns. However, detailed characterizations of how population-level behaviors change over time during multiple disease outbreaks and spatial resolutions are still not widely available. We used data from 431,211 survey responses collected in the United States, between April 2020 and June 2022, to provide a description of how human behaviors fluctuated during the first 2 y of the COVID-19 pandemic. Our analysis suggests that at the national and state levels, people's adherence to recommendations to avoid contact with others (a preventive behavior) was highest early in the pandemic but gradually-and linearly-decreased over time. Importantly, during periods of intense COVID-19 mortality, adaption to preventive behaviors increased-despite the overall temporal decrease. These spatial-temporal characterizations help improve our understanding of the bidirectional feedback loop between outbreak severity and human behavior. Our findings should benefit both computational modeling teams developing methodologies to predict the dynamics of future epidemics and policymakers designing strategies to mitigate the effects of future disease outbreaks.